53 lines
2 KiB
Python
53 lines
2 KiB
Python
""" This module contains the function to classify the user query. """
|
|
|
|
import json
|
|
|
|
from langchain.prompts import ChatPromptTemplate
|
|
from langchain.chains import create_extraction_chain
|
|
from langchain.chat_models import ChatOpenAI
|
|
from langchain.document_loaders import TextLoader
|
|
from langchain.document_loaders import DirectoryLoader
|
|
|
|
from ..config import Config
|
|
|
|
config = Config()
|
|
config.load()
|
|
OPENAI_API_KEY = config.openai_key
|
|
|
|
async def classify_user_query(query, context, document_types):
|
|
"""Classify the user query based on the context and document types."""
|
|
llm = ChatOpenAI(temperature=0, model=config.model)
|
|
prompt_classify = ChatPromptTemplate.from_template(
|
|
"""You are a classifier.
|
|
You store user memories, thoughts and feelings.
|
|
Determine if you need to use them to answer this query : {query}"""
|
|
)
|
|
json_structure = [
|
|
{
|
|
"name": "classifier",
|
|
"description": "Classification",
|
|
"parameters": {
|
|
"type": "object",
|
|
"properties": {
|
|
"UserQueryClassifier": {
|
|
"type": "bool",
|
|
"description": "The classification of documents "
|
|
"in groups such as legal, medical, etc.",
|
|
}
|
|
},
|
|
"required": ["UserQueryClassifier"],
|
|
},
|
|
}
|
|
]
|
|
chain_filter = prompt_classify | llm.bind(
|
|
function_call={"name": "classifier"}, functions=json_structure
|
|
)
|
|
classifier_output = await chain_filter.ainvoke(
|
|
{"query": query, "context": context, "document_types": document_types}
|
|
)
|
|
arguments_str = classifier_output.additional_kwargs["function_call"]["arguments"]
|
|
print("This is the arguments string", arguments_str)
|
|
arguments_dict = json.loads(arguments_str)
|
|
classfier_value = arguments_dict.get("UserQueryClassifier", None)
|
|
print("This is the classifier value", classfier_value)
|
|
return classfier_value
|